This paper presents a comprehensive study on clustering: exiting methods and
developments made at various times. Clustering is defined as an unsupervised learning …

Clustering is an unsupervised learning technology, and it groups information (observations
or datasets) according to similarity measures. Developing clustering algorithms is a hot topic …

Density based clustering methods are proposed for clustering spatial databases with noise.
Density Based Spatial Clustering of Applications with Noise (DBSCAN) can discover …

Clustering is an important technique to deal with large scale data which are explosively
created in internet. Most data are high-dimensional with a lot of noise, which brings great …

The ecosystem health of coastal metropolitan regions is changing, which will exert a
negative impact on human survival and development in the future. In this case, it is a novel …

Clustering analysis has been widely used in statistics, machine learning, pattern recognition,
image processing, and so on. It is a great challenge for most existing clustering algorithms to …

We study the problem of implementing graph algorithms efficiently on Pregel-like systems,
which can be surprisingly challenging. Standard graph algorithms in this setting can incur …

Minimum spanning trees (MSTs) have long been used in data mining, pattern recognition
and machine learning. However, it is difficult to apply traditional MST algorithms to a large …

In this paper, an adaptive spatial clustering algorithm based on Delaunay triangulation
(ASCDT for short) is proposed. The ASCDT algorithm employs both statistical features of the …

Geometrical properties and attributes are two important characteristics of a spatial object. In
previous spatial clustering studies, these two characteristics were often neglected. This …